1. Field of the Invention
The present invention relates to schemes for automatically and intelligently searching for the best versions of websites by optimizing user defined criteria.
2. Background
The online advertisement industry is experiencing a dramatic growth fueled by its low cost and interactivity. While a great deal of time and money has been dedicated to increasing website traffic, heretofore, little attention has been paid to increasing website conversion rates (i.e., the rates at which website visitors engage in desired business activity while visiting the website).
Optimizing a website to increase conversion rates is a very complex undertaking. One problem is that visitor demographics and preferences are not known. Another problem is that each page of a website consists of a large number of variable factors, such as page content, visuals, call-for-action links (callouts), fonts, and page layouts. Combinations of these factors create millions of possible variations on the look and feel of a particular web page. Yet, empirical evidence shows that a minute change in the website look and feel, such as the color of a “submit” button, for example, can dramatically change the conversion rate of the web page.
An additional challenge to website optimization is the real-time nature of the online marketing. Often, a major marketing promotion can be finished in matter of minutes or hours, leaving no time for any manual experimentation or analysis. And finally, making any changes to a website is a very labor intensive process.
Companies are attempting to increase website conversion through variety of approaches. Some approaches rely entirely on the experience of consultants and designers who apply their rich empirical knowledge to produce a website expected to have a high conversion rate. Such an approach, however, is a subjective one. What is successful for one company may not be effective for another. Further, even if a new version is more successful than a previous version, it is not clear as to whether the website has reached the maximum conversion rate.
Others apply scientific methods of measuring website conversion, and run experiments for the purpose of finding a version of the website that has the highest conversion rate. The simplest method is the so called “A/B” testing method. In essence, this is a brute force method where two (or some small number) of the most likely versions of the website are tested to find the better one. Normally, the A/B experiments are perpetually performed in a never-ending search for a better solution. These experiments are labor intensive, and their search methodology is quite inefficient. If one is testing a few out of millions of possible variations of the single page, it is easily understandable that the probability of finding the best solution is quite low.
A much better method is the multivariable (or multivariate) testing method offered by companies such as Google, Memetrics, Optimost LLC, SiteSpect, Offermatica, and others. This Letters Patent hereby classifies those methodologies as an open loop approach, or a batch processing website optimization method. The open loop methodology relies on the following key optimization steps: experiment design; experiment execution; and statistical analysis and modeling.
“Open-loop” is a well-known term of art from the field of automatic control systems. Consistent with the definition, an open-loop controller does not utilize feedback to determine if its output has achieved the desired goal of its input, or stated another way, an open-loop system does not observe the output of the processes that it is controlling.
In the experiment design a vendor may choose a full factorial approach (i.e. test all possible variations of a page or website pages) or fractional factorial approach, where only a subset of possible page variations is tested. Vendors might use different approaches in designing the optimization experiments, but the common outcome is a creation of a list of the web page (web site) versions (i.e., different page impressions), that will be tested. In the next step, all page impressions from the said list are tested, and measurement data is collected. During the statistical analysis and modeling phase, the experimental data is processed to predict the best variation of the page (or website). Different statistical methods may be used in the statistical analysis step, such as, for example, the Taguchi method, Bayesian Markov Chain Monte Carlo estimation procedures, or a Genetic Algorithm.
This open-loop approach suffers from a number of disadvantages. Since statistical characteristics of the variable elements included in the experiment are not known beforehand, the experiment designers are forced to create the experiments containing a large number of the page impressions that are participating in the experiment. As a result, the experimentation is time consuming, and more expensive than is desirable. The methodology involves static optimization (e.g., test once, and use the best solution), while the online marketplace is very dynamic, with rapidly changing user preferences and requirements. Such an approach will not be able to detect or respond to the aforementioned changes. Finally, the approach is hardwired for a specific statistical methodology, and such a “one size fits all” approach might not work well for different types of customers.
Several prior art Letters Patents related to website optimization are discussed below. U.S. Pat. No. 6,934,748 to Louviere, et al. discloses an automated open loop (i.e., no feedback) system for experimentation that includes an experiment engine which can define an experiment relating to various treatments for a set of content elements. U.S. Patent Application Number 20040123247 by Wachen discloses a method and apparatus for altering electronic content that includes a template for assigning variables and values to a section of the content, a generator that creates the permutations of the content, a transmitter that provides the content to a requestor and an evaluator and optimizer that aids in selecting the most optimal permutation of the content. U.S. Patent Application Number 20060271671 by Hansen, discloses a method and system for optimizing web visitor conversion using a reverse proxy server to introduce page variations on existing website content without the need to modify the existing target server.
U.S. Patent Application Number 20030014304 by Calvert, et al. discloses a method of evaluating Internet advertisement effectiveness that involves collecting Internet activity information associated with a multitude of “cookies.” U.S. Pat. No. 5,968,125 to Garrick, et al. discloses a process for determining the effectiveness of a web page to a visitor by creating alternative and test web pages, sending requests to the test web page, and monitoring the use of the web page and the rate that the web page objective was met and replacing pages with the page most visited. U.S. Patent Application Number 20020042738 by Srinivasan, et al. discloses a method and system for measuring the effectiveness of the layout or appearance of a website advertisement to a visitor, wherein different visitors are shown different formats of the same page, response to the page is monitored, and statistics are analyzed regarding the responses.
U.S. Patent Application Number 20030018501 by Shan discloses a method and system for processing test data relevant to specific behavior of visitors of a website. U.S. Pat. No. 6,662,215 to Moskowitz, et al. discloses a system and method for determining appropriate website content for consumers comprising a server arrangement, including a “real time content optimization” server, a user computer, and a network, wherein upon request a web page is generated for the user having static and dynamic elements which are tested for user reaction and response.